←Search results

Search “Meta”

By relevance · Total 2294 results Clear search

Friday

  2. UnslothSelected

    Meta releases open-source model Muse Spark Open, supporting 1M context and multimodal input

    Meta releases open-source model Muse Spark Open, with weights released the same day. Muse-Spark-Open-816B-A42B supports text, image, video and audio input, with a context window of 1M. Unsloth provides a Dynamic 2-bit GGUF quantized version that can run on Mac Studio, along with a GGUF download address and usage guide.

    Matched fieldsTitle、Summary、Body

    #company#Model releases#Multimodal#Open-source ecosystem
  5. Rohan PaulSelected

    Meta open-sources 816B-parameter model Muse Spark Open on Hugging Face

    Meta has released the model weights of Muse Spark Open on Hugging Face, an 816B-parameter model aimed at coding and agent tasks. The open-source version shares the same base model as Muse Spark 1.3, with additional safety post-training to reduce the risk of misuse when used standalone without system guardrails, reversing Meta's strategy of its first proprietary closed frontier model in April this year. The model has 62 layers, each containing 256 expert networks, with each token routed to 8 of them, so deployment requires the memory of an 816B-parameter model but runs at the speed of a 42B model. A 3-bit quantized version tested on a 512GB M3 Ultra occupies about 344 GB, with generation speed of about 21 tok/s under short prompts.

    Meta has released the model weights of Muse Spark Open on Hugging Face, an 816B-parameter model aimed at coding and agent tasks. The open-source version shares the same base model as Muse Spark 1.3, with additional safety post-training to reduce the risk of misuse when used standalone without system guardrails, reversing Meta's strategy of its first proprietary closed frontier model in April this year. The model has 62 layers, each containing 256 expert networks, with each token routed to 8 of t

    Matched fieldsTitle、Summary、Body

    #ai-news#Model releases#Open-source ecosystem#English text is being prepared.
  6. Rohan PaulSelected

    Meta open-sources 816B-parameter model Muse Spark Open, adapted for Apple MLX to run on Mac

    Meta has open-sourced the 816B-parameter Muse Spark Open on Hugging Face, sharing the same base model as Muse Spark 1.3 with added safety post-training. The model has 256 expert networks per layer, routing each token to 8 of them, so deployment requires 816B-parameter-level VRAM but runs at the speed of a 42B model; the 4-bit NVFP4 version requires at least 700GB of GPU VRAM, and Apple has adapted it for its MLX framework, with two M5 Ultra Mac Studios each providing up to 512GB of unified memory and 1.2TB/s bandwidth to carry it. It tops the MCP-Atlas tool-calling test at 89.2, and the license allows free commercial use but requires attribution as Built with Muse Spark Open; API model sellers with over $20 million in revenue over 12 months must sign a separate agreement with Meta.

    Meta has open-sourced the 816B-parameter Muse Spark Open on Hugging Face, sharing the same base model as Muse Spark 1.3 with added safety post-training. The model has 256 expert networks per layer, routing each token to 8 of them, so deployment requires 816B-parameter-level VRAM but runs at the speed of a 42B model; the 4-bit NVFP4 version requires at least 700GB of GPU VRAM, and Apple has adapted it for its MLX framework, with two M5 Ultra Mac Studios each providing up to 512GB of unified memor

    Matched fieldsTitle、Summary、Body

    #ai-news#Model releases#Open-source ecosystem#English text is being prepared.
  12. Unite.AISelected

    Meta open-sources 816B-parameter multimodal model Muse Spark Open

    On October 15, Meta released the weights of Muse Spark Open, a natively multimodal open-source model with 816B total parameters, 42B activated parameters, and a 1M token context window, targeting coding and long-horizon agent tasks. The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running the NVFP4 version locally requires at least 700GB of VRAM. Meta claims it is the first model of its class that can run on desktop, carried by two Mac Studios equipped with M5 Ultra paired with the MLX framework; the model card shows its Artificial Analysis Intelligence Index at 39, lower than GLM 5.3's 45 and Kimi K3's 44 in the table.

    On October 15, Meta released the weights of Muse Spark Open, a natively multimodal open-source model with 816B total parameters, 42B activated parameters, and a 1M token context window, targeting coding and long-horizon agent tasks. The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running th

    Matched fieldsTitle、Summary、Body

    #hot-signal#Model releases#Multimodal#Open-source ecosystem
  14. The New StackSelected

    Meta releases 816B open-source model Muse Spark Open

    Meta releases Muse Spark Open, an open-weight model with 816B parameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchmarks such as Terminal-Bench 2.1 and DeepSWE 1.1. The model uses the Muse Model License; companies with annual revenue over $20 million must first notify Meta and sign a separate agreement to offer model services, and users must prominently label Built with Muse Spark Open in product interfaces.

    Meta releases Muse Spark Open, an open-weight model with 816B parameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchma

    Matched fieldsTitle、Summary、Body

    #hot-signal#Model releases#Open-source ecosystem#Reasoning

Thursday